##########################################
# extract counts and proportions for plotting
# composite reference_df
agg_reference_df <- dplyr::left_join(
total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Year"),
names_to = "Type", values_to = "Count"),
total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year) %>%
dplyr::summarize(climate_references = mean(climate_count, na.rm = T),
health_references = mean(health_count, na.rm = T),
intersection_references = mean(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Year"),
names_to = "Type", values_to = "Avg")) %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L))
# health sector reference_df
reference_df <- dplyr::left_join(
total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Sector, Year) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Sector", "Year"),
names_to = "Type", values_to = "Count"),
total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Sector, Year) %>%
dplyr::summarize(climate_references = mean(climate_count, na.rm = T),
health_references = mean(health_count, na.rm = T),
intersection_references = mean(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Sector", "Year"),
names_to = "Type", values_to = "Avg")) %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L))
# composite plot_df
agg_plot_df <- total_texts %>% dplyr::filter(Year >= 2011) %>%
tidyr::pivot_longer(cols = -c("Sector", "Year", "total_texts"),
names_to = "text", values_to = "value") %>%
dplyr::group_by(Year, text) %>%
dplyr::summarise(total_texts = sum(total_texts, na.rm = T),
value = sum(value, na.rm = T)) %>%
dplyr::mutate(value = tidyr::replace_na(value, 0),
Prop = value/total_texts,
Key = factor(dplyr::case_when(text == "climate_texts" ~ "Climate",
text == "intersection_texts" ~ "Intersection",
text == "health_texts" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L))
# health sector plot_df
plot_df <- total_texts %>% dplyr::filter(Year >= 2011) %>%
tidyr::pivot_longer(cols = -c("Sector", "Year", "total_texts"),
names_to = "text", values_to = "value") %>%
dplyr::mutate(value = tidyr::replace_na(value, 0),
Prop = value/total_texts,
Key = factor(dplyr::case_when(text == "climate_texts" ~ "Climate",
text == "intersection_texts" ~ "Intersection",
text == "health_texts" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L))data_summary <- tibble::tibble(
count = length(total_counts$Participant),
unique = unique(total_counts$Participant) %>% length(),
total_health = total_counts %>% dplyr::filter(!is.na(health_count)) %>% dplyr::filter(health_count != 0) %>% nrow(),
total_climate = total_counts %>% dplyr::filter(!is.na(climate_count)) %>% dplyr::filter(climate_count != 0) %>% nrow(),
total_intersection = total_counts %>% dplyr::filter(!is.na(intersection_count)) %>% dplyr::filter(intersection_count != 0) %>% nrow()
) %>%
dplyr::mutate(prop_health = round(total_health/count,2),
prop_climate = round(total_climate/count,2),
prop_intersection = round(total_intersection/count,2))
colnames(data_summary) <- c("Companies (N)", "Companies (Unique)",
"Health, (N)" , "Climate, (N)", "Intersection, (N)",
"Health, %" , "Climate, %", "Intersection, %")
data_summary %>%
knitr::kable()| Companies (N) | Companies (Unique) | Health, (N) | Climate, (N) | Intersection, (N) | Health, % | Climate, % | Intersection, % |
|---|---|---|---|---|---|---|---|
| 17984 | 5329 | 14528 | 12297 | 4627 | 0.81 | 0.68 | 0.26 |
readr::write_csv(data_summary, "../output/0_0_data_summary.csv")a <- total_counts %>% dplyr::filter(Year >= 2011) %>% dplyr::group_by(Year) %>%
dplyr::summarize("Companies (N)" = n_distinct(Participant))
b <- agg_plot_df %>%
dplyr::select(Year, Key, value) %>%
dplyr::mutate(Year = lubridate::year(Year)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "value") %>%
dplyr::rename("Climate (N)" = "Climate", "Health (N)" = "Health", "Intersection (N)" = "Intersection")
c <- agg_plot_df %>%
dplyr::select(Year, Key, Prop) %>%
dplyr::mutate(Prop = round(Prop, 2),
Year = lubridate::year(Year)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "Prop") %>%
dplyr::rename("Climate (Prop)" = "Climate", "Health (Prop)" = "Health", "Intersection (Prop)" = "Intersection")
yearly_breakdown <- dplyr::left_join(a, b) %>% left_join(., c)
yearly_breakdown %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Companies (N) | Climate (N) | Health (N) | Intersection (N) | Climate (Prop) | Health (Prop) | Intersection (Prop) |
|---|---|---|---|---|---|---|---|
| 2011 | 1021 | 773 | 868 | 282 | 0.73 | 0.82 | 0.27 |
| 2012 | 1374 | 998 | 1150 | 327 | 0.70 | 0.81 | 0.23 |
| 2013 | 1553 | 1069 | 1287 | 343 | 0.67 | 0.80 | 0.21 |
| 2014 | 1686 | 1131 | 1369 | 357 | 0.66 | 0.79 | 0.21 |
| 2015 | 1771 | 1194 | 1463 | 408 | 0.66 | 0.81 | 0.22 |
| 2016 | 1928 | 1266 | 1546 | 440 | 0.64 | 0.78 | 0.22 |
| 2017 | 1972 | 1352 | 1594 | 484 | 0.67 | 0.79 | 0.24 |
| 2018 | 2000 | 1392 | 1638 | 553 | 0.68 | 0.80 | 0.27 |
| 2019 | 2197 | 1568 | 1864 | 637 | 0.70 | 0.83 | 0.28 |
| 2020 | 2029 | 1547 | 1742 | 791 | 0.75 | 0.84 | 0.38 |
readr::write_csv(yearly_breakdown, "../output/0_1_yearly_breakdown.csv")reports_by_year <- total_counts %>% dplyr::filter(Year >= 2011) %>% dplyr::group_by(Year) %>%
dplyr::summarize(count = n_distinct(Participant)) %>%
dplyr::rename("Companies (N)" = "count")
reports_by_year %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Companies (N) |
|---|---|
| 2011 | 1021 |
| 2012 | 1374 |
| 2013 | 1553 |
| 2014 | 1686 |
| 2015 | 1771 |
| 2016 | 1928 |
| 2017 | 1972 |
| 2018 | 2000 |
| 2019 | 2197 |
| 2020 | 2029 |
readr::write_csv(reports_by_year, "../output/0_2_reports_by_year.csv")## a. Main text - Proportion of companies, %
p <- agg_plot_df %>%
ggplot(aes(x = Year, y = Prop, color = Key)) +
geom_path(aes(linetype = Key)) +
scale_linetype_manual(values = c("solid", "longdash", "twodash")) +
ggplot2::annotate("text", x = as.Date("2013-05-31"), y = 0.85, label = "Health", color = "#619cff") +
ggplot2::annotate("text", x = as.Date("2013-05-31"), y = 0.48, label = "Climate Change", color = "darkgreen") +
ggplot2::annotate("text", x = as.Date("2012-12-31"), y = 0.1, label = "Intersection", color = "red") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0,1)) +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Proportion of companies, %")
pggsave("../output/1a_prop_of_companies.pdf", p, width = 10, height = 7)t <- agg_plot_df %>%
dplyr::select(Year, Key, Prop) %>%
dplyr::mutate(Prop = round(Prop, 2),
Year = lubridate::year(Year)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "Prop")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Climate | Health | Intersection |
|---|---|---|---|
| 2011 | 0.73 | 0.82 | 0.27 |
| 2012 | 0.70 | 0.81 | 0.23 |
| 2013 | 0.67 | 0.80 | 0.21 |
| 2014 | 0.66 | 0.79 | 0.21 |
| 2015 | 0.66 | 0.81 | 0.22 |
| 2016 | 0.64 | 0.78 | 0.22 |
| 2017 | 0.67 | 0.79 | 0.24 |
| 2018 | 0.68 | 0.80 | 0.27 |
| 2019 | 0.70 | 0.83 | 0.28 |
| 2020 | 0.75 | 0.84 | 0.38 |
readr::write_csv(t, "../output/1a_prop_of_companies.csv")## b. Appendix - Total number of references
p <- agg_reference_df %>%
ggplot(aes(x = Year, y = Count, color = Key)) +
geom_line(aes(linetype = Key)) +
scale_linetype_manual(values = c("solid", "longdash", "twodash")) +
ggplot2::annotate("text", x = as.Date("2011-05-31"), y = 31500, label = "Health", color = "#619cff") +
ggplot2::annotate("text", x = as.Date("2013-05-31"), y = 25000, label = "Climate Change", color = "darkgreen") +
ggplot2::annotate("text", x = as.Date("2016-12-31"), y = 4000, label = "Intersection", color = "red") +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Total number of references")
pggsave("../output/1b_number_of_references.pdf", p, width = 10, height = 7)t <- agg_plot_df %>%
dplyr::select(Year, Key, value) %>%
dplyr::mutate(Year = lubridate::year(Year)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "value")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Climate | Health | Intersection |
|---|---|---|---|
| 2011 | 773 | 868 | 282 |
| 2012 | 998 | 1150 | 327 |
| 2013 | 1069 | 1287 | 343 |
| 2014 | 1131 | 1369 | 357 |
| 2015 | 1194 | 1463 | 408 |
| 2016 | 1266 | 1546 | 440 |
| 2017 | 1352 | 1594 | 484 |
| 2018 | 1392 | 1638 | 553 |
| 2019 | 1568 | 1864 | 637 |
| 2020 | 1547 | 1742 | 791 |
readr::write_csv(t, "../output/1b_number_of_references.csv")## c. Appendix - Total number of references (Intersection)
p <- agg_reference_df %>%
dplyr::filter(Key == "Intersection") %>%
ggplot(aes(x = Year, y = Count, color = Key)) +
geom_line() +
ggplot2::annotate("text", x = as.Date("2013-12-31"), y = 1500, label = "Intersection", color = "red") +
theme_minimal() +
scale_y_continuous(limits = c(0, 4000)) +
theme(legend.position = "none") +
labs(y = "Total number of references")
pggsave("../output/1c_number_of_references_intersection.pdf", p, width = 10, height = 7)t <- agg_reference_df %>%
dplyr::select(Year, Key, Count) %>%
dplyr::mutate(Year = lubridate::year(Year)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "Count") %>%
dplyr::select(Year, Intersection)
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Intersection |
|---|---|
| 2011 | 1023 |
| 2012 | 989 |
| 2013 | 1320 |
| 2014 | 1321 |
| 2015 | 1305 |
| 2016 | 1571 |
| 2017 | 1739 |
| 2018 | 1828 |
| 2019 | 2421 |
| 2020 | 3365 |
readr::write_csv(t, "../output/1c_number_of_references_intersection.csv")p <- agg_plot_df %>%
dplyr::filter(Key == "Intersection") %>%
ggplot(aes(x = Year, y = Prop, color = Key)) +
geom_path(aes(linetype = Key)) +
scale_linetype_manual(values = c("solid", "longdash", "twodash")) +
ggplot2::annotate("text", x = as.Date("2015-12-31"), y = 0.3, label = "Intersection", color = "red") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 0.4)) +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Proportion of companies, %")
pggsave("../output/1d_proportion_intersection.pdf", p, width = 10, height = 7)agg_plot_df %>%
dplyr::select(Year, Key, Prop) %>%
dplyr::mutate(Year = lubridate::year(Year),
Prop = round(Prop, 2)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "Prop") %>%
dplyr::select(Year, Intersection) t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Intersection |
|---|---|
| 2011 | 1023 |
| 2012 | 989 |
| 2013 | 1320 |
| 2014 | 1321 |
| 2015 | 1305 |
| 2016 | 1571 |
| 2017 | 1739 |
| 2018 | 1828 |
| 2019 | 2421 |
| 2020 | 3365 |
readr::write_csv(t, "../output/1d_proportion_intersection.csv")p <- agg_reference_df %>%
ggplot(aes(x = Year, y = Avg, color = Key)) +
geom_line(aes(linetype = Key)) +
scale_linetype_manual(values = c("solid", "longdash", "twodash")) +
ggplot2::annotate("text", x = as.Date("2011-05-31"), y = 29, label = "Health", color = "#619cff") +
ggplot2::annotate("text", x = as.Date("2014-01-01"), y = 17, label = "Climate Change", color = "darkgreen") +
ggplot2::annotate("text", x = as.Date("2016-12-31"), y = 3, label = "Intersection", color = "red") +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Average number of references")
pggsave("../output/1e_avg_references.pdf", p, width = 10, height = 7)t <- agg_reference_df %>%
dplyr::select(Year, Key, Avg) %>%
dplyr::mutate(Year = lubridate::year(Year),
Avg = round(Avg,2)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "Avg")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Climate | Health | Intersection |
|---|---|---|---|
| 2011 | 20.26 | 26.96 | 1.10 |
| 2012 | 17.06 | 24.99 | 0.80 |
| 2013 | 15.05 | 24.05 | 0.96 |
| 2014 | 15.46 | 25.95 | 0.90 |
| 2015 | 13.87 | 24.89 | 0.84 |
| 2016 | 14.41 | 24.19 | 0.95 |
| 2017 | 14.53 | 24.55 | 1.02 |
| 2018 | 15.21 | 25.98 | 1.05 |
| 2019 | 17.48 | 26.14 | 1.24 |
| 2020 | 22.94 | 32.98 | 1.84 |
readr::write_csv(t, "../output/1e_avg_references.csv")## f. Appendix - Total number of references by WHO region
p <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year, who_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Year", "who_region"),
names_to = "Type", values_to = "Count") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection") %>%
ggplot(aes(x = Year, y = Count, color = who_region)) +
geom_path() +
theme_minimal() +
labs(y = "Total number of references",
color = "WHO Region")
pggsave("../output/1f_who_number_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year, who_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
dplyr::arrange(who_region, Year)
colnames(t) <- c("Year", "WHO Region", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | WHO Region | Climate | Health | Intersection |
|---|---|---|---|---|
| 2011 | Africa | 444 | 788 | 56 |
| 2012 | Africa | 242 | 960 | 20 |
| 2013 | Africa | 429 | 1460 | 38 |
| 2014 | Africa | 552 | 1536 | 73 |
| 2015 | Africa | 663 | 1880 | 50 |
| 2016 | Africa | 686 | 1988 | 41 |
| 2017 | Africa | 486 | 1383 | 51 |
| 2018 | Africa | 397 | 1210 | 31 |
| 2019 | Africa | 551 | 1527 | 52 |
| 2020 | Africa | 459 | 907 | 42 |
| 2011 | Eastern Mediterranean | 124 | 475 | 14 |
| 2012 | Eastern Mediterranean | 236 | 945 | 18 |
| 2013 | Eastern Mediterranean | 201 | 1133 | 29 |
| 2014 | Eastern Mediterranean | 196 | 721 | 13 |
| 2015 | Eastern Mediterranean | 290 | 1083 | 10 |
| 2016 | Eastern Mediterranean | 398 | 1073 | 29 |
| 2017 | Eastern Mediterranean | 349 | 1048 | 28 |
| 2018 | Eastern Mediterranean | 793 | 1662 | 46 |
| 2019 | Eastern Mediterranean | 748 | 1675 | 47 |
| 2020 | Eastern Mediterranean | 478 | 1450 | 51 |
| 2011 | Europe | 9455 | 12354 | 525 |
| 2012 | Europe | 10235 | 15910 | 547 |
| 2013 | Europe | 10684 | 15754 | 657 |
| 2014 | Europe | 12147 | 18610 | 680 |
| 2015 | Europe | 11079 | 16718 | 626 |
| 2016 | Europe | 12369 | 18716 | 803 |
| 2017 | Europe | 12299 | 19129 | 836 |
| 2018 | Europe | 12988 | 20714 | 891 |
| 2019 | Europe | 16866 | 24566 | 1217 |
| 2020 | Europe | 21566 | 28534 | 1597 |
| 2011 | Latin America and the Caribbean | 563 | 1214 | 41 |
| 2012 | Latin America and the Caribbean | 1339 | 2128 | 52 |
| 2013 | Latin America and the Caribbean | 876 | 1703 | 60 |
| 2014 | Latin America and the Caribbean | 804 | 1719 | 65 |
| 2015 | Latin America and the Caribbean | 703 | 1661 | 15 |
| 2016 | Latin America and the Caribbean | 958 | 1656 | 38 |
| 2017 | Latin America and the Caribbean | 1306 | 1961 | 64 |
| 2018 | Latin America and the Caribbean | 1179 | 2514 | 102 |
| 2019 | Latin America and the Caribbean | 1092 | 2837 | 76 |
| 2020 | Latin America and the Caribbean | 1756 | 3554 | 152 |
| 2011 | North America | 1755 | 3479 | 125 |
| 2012 | North America | 1853 | 3416 | 123 |
| 2013 | North America | 1589 | 4206 | 152 |
| 2014 | North America | 1840 | 5760 | 134 |
| 2015 | North America | 2188 | 7050 | 253 |
| 2016 | North America | 2341 | 6677 | 265 |
| 2017 | North America | 2553 | 6623 | 281 |
| 2018 | North America | 2538 | 6043 | 247 |
| 2019 | North America | 3562 | 5848 | 311 |
| 2020 | North America | 3978 | 6842 | 398 |
| 2011 | South-East Asia | 824 | 1527 | 57 |
| 2012 | South-East Asia | 868 | 1769 | 56 |
| 2013 | South-East Asia | 1201 | 2353 | 76 |
| 2014 | South-East Asia | 937 | 2272 | 55 |
| 2015 | South-East Asia | 1158 | 3050 | 105 |
| 2016 | South-East Asia | 1415 | 2648 | 116 |
| 2017 | South-East Asia | 944 | 2820 | 111 |
| 2018 | South-East Asia | 1073 | 2642 | 110 |
| 2019 | South-East Asia | 1230 | 2911 | 128 |
| 2020 | South-East Asia | 1123 | 2448 | 166 |
| 2011 | Western Pacific | 5755 | 5341 | 205 |
| 2012 | Western Pacific | 6308 | 5757 | 173 |
| 2013 | Western Pacific | 5641 | 6341 | 308 |
| 2014 | Western Pacific | 6097 | 7262 | 301 |
| 2015 | Western Pacific | 5489 | 7263 | 246 |
| 2016 | Western Pacific | 5579 | 7100 | 279 |
| 2017 | Western Pacific | 6748 | 8739 | 368 |
| 2018 | Western Pacific | 7476 | 10397 | 401 |
| 2019 | Western Pacific | 10069 | 11653 | 590 |
| 2020 | Western Pacific | 12474 | 16419 | 959 |
readr::write_csv(t, "../output/1f_who_number_references.csv")## j. Appendix - Total number of regerences by by SIDS, Tier 1 and Tier 2 categories
p <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, who_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
tidyr::pivot_longer(cols = -c("Year", "who_region"),
names_to = "Type", values_to = "Prop") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_prop" ~ "Climate",
Type == "intersection_prop" ~ "Intersection",
Type == "health_prop" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection" & !is.na(who_region)) %>%
ggplot(aes(x = Year, y = Prop, color = who_region)) +
geom_line() +
theme_minimal() +
labs(y = "Proportion of companies, %",
color = "") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 0.5))
pggsave("../output/1g_who_prop_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, who_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
dplyr::arrange(desc(who_region), Year) %>%
dplyr::filter(!is.na(who_region))
colnames(t) <- c("Year", "WHO Region", "Total documents", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | WHO Region | Total documents | Climate | Health | Intersection |
|---|---|---|---|---|---|
| 2011 | Western Pacific | 165 | 0.84 | 0.87 | 0.38 |
| 2012 | Western Pacific | 215 | 0.78 | 0.84 | 0.30 |
| 2013 | Western Pacific | 222 | 0.77 | 0.86 | 0.34 |
| 2014 | Western Pacific | 225 | 0.76 | 0.85 | 0.35 |
| 2015 | Western Pacific | 225 | 0.74 | 0.86 | 0.36 |
| 2016 | Western Pacific | 248 | 0.73 | 0.82 | 0.30 |
| 2017 | Western Pacific | 257 | 0.79 | 0.86 | 0.37 |
| 2018 | Western Pacific | 265 | 0.79 | 0.85 | 0.41 |
| 2019 | Western Pacific | 300 | 0.77 | 0.86 | 0.41 |
| 2020 | Western Pacific | 279 | 0.84 | 0.91 | 0.57 |
| 2011 | South-East Asia | 91 | 0.62 | 0.76 | 0.24 |
| 2012 | South-East Asia | 107 | 0.59 | 0.79 | 0.20 |
| 2013 | South-East Asia | 129 | 0.63 | 0.82 | 0.22 |
| 2014 | South-East Asia | 134 | 0.55 | 0.83 | 0.16 |
| 2015 | South-East Asia | 156 | 0.58 | 0.78 | 0.21 |
| 2016 | South-East Asia | 151 | 0.56 | 0.79 | 0.23 |
| 2017 | South-East Asia | 145 | 0.57 | 0.86 | 0.23 |
| 2018 | South-East Asia | 146 | 0.55 | 0.84 | 0.20 |
| 2019 | South-East Asia | 156 | 0.59 | 0.86 | 0.28 |
| 2020 | South-East Asia | 132 | 0.58 | 0.86 | 0.26 |
| 2011 | North America | 101 | 0.76 | 0.83 | 0.30 |
| 2012 | North America | 133 | 0.70 | 0.83 | 0.26 |
| 2013 | North America | 139 | 0.65 | 0.78 | 0.26 |
| 2014 | North America | 141 | 0.64 | 0.82 | 0.19 |
| 2015 | North America | 153 | 0.67 | 0.82 | 0.26 |
| 2016 | North America | 156 | 0.67 | 0.84 | 0.27 |
| 2017 | North America | 160 | 0.79 | 0.86 | 0.37 |
| 2018 | North America | 167 | 0.73 | 0.83 | 0.33 |
| 2019 | North America | 188 | 0.76 | 0.84 | 0.33 |
| 2020 | North America | 181 | 0.87 | 0.90 | 0.46 |
| 2011 | Latin America and the Caribbean | 61 | 0.56 | 0.66 | 0.23 |
| 2012 | Latin America and the Caribbean | 104 | 0.55 | 0.62 | 0.23 |
| 2013 | Latin America and the Caribbean | 95 | 0.55 | 0.62 | 0.22 |
| 2014 | Latin America and the Caribbean | 106 | 0.48 | 0.60 | 0.16 |
| 2015 | Latin America and the Caribbean | 82 | 0.54 | 0.66 | 0.13 |
| 2016 | Latin America and the Caribbean | 137 | 0.42 | 0.46 | 0.14 |
| 2017 | Latin America and the Caribbean | 133 | 0.50 | 0.50 | 0.19 |
| 2018 | Latin America and the Caribbean | 143 | 0.49 | 0.50 | 0.21 |
| 2019 | Latin America and the Caribbean | 127 | 0.48 | 0.50 | 0.23 |
| 2020 | Latin America and the Caribbean | 133 | 0.56 | 0.60 | 0.32 |
| 2011 | Europe | 557 | 0.75 | 0.82 | 0.25 |
| 2012 | Europe | 743 | 0.74 | 0.82 | 0.22 |
| 2013 | Europe | 863 | 0.71 | 0.81 | 0.19 |
| 2014 | Europe | 950 | 0.70 | 0.80 | 0.21 |
| 2015 | Europe | 997 | 0.69 | 0.80 | 0.22 |
| 2016 | Europe | 1068 | 0.68 | 0.79 | 0.22 |
| 2017 | Europe | 1100 | 0.69 | 0.79 | 0.22 |
| 2018 | Europe | 1095 | 0.72 | 0.82 | 0.26 |
| 2019 | Europe | 1236 | 0.73 | 0.84 | 0.27 |
| 2020 | Europe | 1181 | 0.77 | 0.85 | 0.37 |
| 2011 | Eastern Mediterranean | 42 | 0.48 | 0.81 | 0.19 |
| 2012 | Eastern Mediterranean | 63 | 0.54 | 0.76 | 0.22 |
| 2013 | Eastern Mediterranean | 80 | 0.40 | 0.78 | 0.11 |
| 2014 | Eastern Mediterranean | 77 | 0.44 | 0.70 | 0.06 |
| 2015 | Eastern Mediterranean | 114 | 0.51 | 0.80 | 0.08 |
| 2016 | Eastern Mediterranean | 118 | 0.50 | 0.80 | 0.12 |
| 2017 | Eastern Mediterranean | 121 | 0.50 | 0.83 | 0.09 |
| 2018 | Eastern Mediterranean | 136 | 0.51 | 0.82 | 0.18 |
| 2019 | Eastern Mediterranean | 131 | 0.56 | 0.84 | 0.19 |
| 2020 | Eastern Mediterranean | 99 | 0.58 | 0.82 | 0.24 |
| 2011 | Africa | 42 | 0.71 | 0.93 | 0.21 |
| 2012 | Africa | 61 | 0.52 | 0.87 | 0.13 |
| 2013 | Africa | 75 | 0.45 | 0.85 | 0.13 |
| 2014 | Africa | 93 | 0.47 | 0.81 | 0.13 |
| 2015 | Africa | 90 | 0.48 | 0.91 | 0.16 |
| 2016 | Africa | 100 | 0.56 | 0.89 | 0.17 |
| 2017 | Africa | 94 | 0.50 | 0.83 | 0.18 |
| 2018 | Africa | 89 | 0.53 | 0.87 | 0.18 |
| 2019 | Africa | 113 | 0.55 | 0.88 | 0.17 |
| 2020 | Africa | 61 | 0.61 | 0.85 | 0.25 |
readr::write_csv(t, "../output/1g_who_prop_references.csv")## i. Appendix - Total number of regerences by SIDS, Tier 1 and Tier 2
p <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year, tier_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Year", "tier_region"),
names_to = "Type", values_to = "Count") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection" & !is.na(tier_region)) %>%
ggplot(aes(x = Year, y = Count, color = tier_region)) +
geom_path() +
theme_minimal() +
labs(y = "Total number of references",
color = "")
pggsave("../output/1h_sids_number_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year, tier_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
dplyr::filter(!is.na(tier_region)) %>%
dplyr::arrange(tier_region, Year)
colnames(t) <- c("Year", "Region", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Region | Climate | Health | Intersection |
|---|---|---|---|---|
| 2011 | SIDS | 274 | 455 | 21 |
| 2012 | SIDS | 428 | 579 | 18 |
| 2013 | SIDS | 350 | 842 | 40 |
| 2014 | SIDS | 376 | 618 | 34 |
| 2015 | SIDS | 350 | 607 | 29 |
| 2016 | SIDS | 203 | 357 | 32 |
| 2017 | SIDS | 258 | 561 | 18 |
| 2018 | SIDS | 417 | 748 | 47 |
| 2019 | SIDS | 676 | 1085 | 63 |
| 2020 | SIDS | 793 | 1288 | 105 |
| 2011 | Tier 1 | 1657 | 3461 | 126 |
| 2012 | Tier 1 | 1799 | 3477 | 126 |
| 2013 | Tier 1 | 1590 | 4118 | 138 |
| 2014 | Tier 1 | 2021 | 6124 | 136 |
| 2015 | Tier 1 | 2287 | 7343 | 263 |
| 2016 | Tier 1 | 2415 | 6739 | 276 |
| 2017 | Tier 1 | 2485 | 6446 | 285 |
| 2018 | Tier 1 | 2469 | 6014 | 222 |
| 2019 | Tier 1 | 2937 | 5411 | 290 |
| 2020 | Tier 1 | 3662 | 6332 | 370 |
| 2011 | Tier 2 | 4973 | 7638 | 369 |
| 2012 | Tier 2 | 4602 | 9329 | 285 |
| 2013 | Tier 2 | 5696 | 9828 | 493 |
| 2014 | Tier 2 | 5930 | 9485 | 322 |
| 2015 | Tier 2 | 5516 | 9505 | 314 |
| 2016 | Tier 2 | 6400 | 10721 | 432 |
| 2017 | Tier 2 | 6292 | 10744 | 466 |
| 2018 | Tier 2 | 6358 | 10888 | 571 |
| 2019 | Tier 2 | 8451 | 13223 | 573 |
| 2020 | Tier 2 | 9395 | 13899 | 729 |
readr::write_csv(t, "../output/1h_sids_number_references.csv")## j. Appendix - Total number of regerences by by SIDS, Tier 1 and Tier 2 categories
p <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, tier_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
tidyr::pivot_longer(cols = -c("Year", "tier_region"),
names_to = "Type", values_to = "Prop") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_prop" ~ "Climate",
Type == "intersection_prop" ~ "Intersection",
Type == "health_prop" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection" & !is.na(tier_region)) %>%
ggplot(aes(x = Year, y = Prop, color = tier_region)) +
geom_line() +
theme_minimal() +
labs(y = "Proportion of companies, %",
color = "") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 0.5))
pggsave("../output/1i_sids_prop_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, tier_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
dplyr::arrange(desc(tier_region), Year) %>%
dplyr::filter(!is.na(tier_region))
colnames(t) <- c("Year", "Category", "Total documents", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Category | Total documents | Climate | Health | Intersection |
|---|---|---|---|---|---|
| 2011 | Tier 2 | 296 | 0.76 | 0.79 | 0.28 |
| 2012 | Tier 2 | 374 | 0.75 | 0.79 | 0.20 |
| 2013 | Tier 2 | 439 | 0.69 | 0.81 | 0.23 |
| 2014 | Tier 2 | 462 | 0.68 | 0.81 | 0.19 |
| 2015 | Tier 2 | 492 | 0.66 | 0.77 | 0.21 |
| 2016 | Tier 2 | 518 | 0.68 | 0.79 | 0.24 |
| 2017 | Tier 2 | 509 | 0.70 | 0.80 | 0.22 |
| 2018 | Tier 2 | 513 | 0.70 | 0.78 | 0.27 |
| 2019 | Tier 2 | 571 | 0.74 | 0.82 | 0.27 |
| 2020 | Tier 2 | 522 | 0.75 | 0.83 | 0.36 |
| 2011 | Tier 1 | 107 | 0.70 | 0.78 | 0.28 |
| 2012 | Tier 1 | 144 | 0.66 | 0.80 | 0.26 |
| 2013 | Tier 1 | 145 | 0.65 | 0.80 | 0.28 |
| 2014 | Tier 1 | 150 | 0.65 | 0.79 | 0.21 |
| 2015 | Tier 1 | 156 | 0.67 | 0.82 | 0.26 |
| 2016 | Tier 1 | 168 | 0.67 | 0.83 | 0.26 |
| 2017 | Tier 1 | 165 | 0.78 | 0.85 | 0.33 |
| 2018 | Tier 1 | 179 | 0.70 | 0.81 | 0.30 |
| 2019 | Tier 1 | 195 | 0.71 | 0.82 | 0.30 |
| 2020 | Tier 1 | 184 | 0.83 | 0.90 | 0.42 |
| 2011 | SIDS | 16 | 0.81 | 0.94 | 0.31 |
| 2012 | SIDS | 24 | 0.71 | 0.88 | 0.25 |
| 2013 | SIDS | 30 | 0.67 | 0.77 | 0.30 |
| 2014 | SIDS | 27 | 0.52 | 0.78 | 0.30 |
| 2015 | SIDS | 34 | 0.56 | 0.79 | 0.21 |
| 2016 | SIDS | 30 | 0.57 | 0.77 | 0.20 |
| 2017 | SIDS | 29 | 0.66 | 0.90 | 0.38 |
| 2018 | SIDS | 34 | 0.74 | 0.94 | 0.38 |
| 2019 | SIDS | 50 | 0.74 | 0.96 | 0.36 |
| 2020 | SIDS | 45 | 0.73 | 0.89 | 0.49 |
readr::write_csv(t, "../output/1i_sids_prop_references.csv")## j. Appendix - Total number of regerences by HDI categories
p <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year, hdi_level) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Year", "hdi_level"),
names_to = "Type", values_to = "Count") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L),
hdi_level = factor(hdi_level, levels = c("Very high human development",
"High human development",
"Medium human development",
"Low human development"))) %>%
dplyr::filter(Key == "Intersection" & !is.na(hdi_level)) %>%
ggplot(aes(x = Year, y = Count, color = hdi_level)) +
geom_line() +
theme_minimal() +
labs(y = "Total number of references",
color = "")
pggsave("../output/1j_hdi_number_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::group_by(Year, hdi_level) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
dplyr::arrange(desc(hdi_level), Year) %>%
dplyr::filter(!is.na(hdi_level))
colnames(t) <- c("Year", "HDI Level", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | HDI Level | Climate | Health | Intersection |
|---|---|---|---|---|
| 2011 | Very high human development | 16600 | 20564 | 820 |
| 2012 | Very high human development | 18267 | 24889 | 833 |
| 2013 | Very high human development | 17807 | 26430 | 1106 |
| 2014 | Very high human development | 19867 | 31121 | 1094 |
| 2015 | Very high human development | 18624 | 30789 | 1097 |
| 2016 | Very high human development | 20204 | 32259 | 1333 |
| 2017 | Very high human development | 21492 | 34297 | 1465 |
| 2018 | Very high human development | 22757 | 36927 | 1531 |
| 2019 | Very high human development | 30285 | 41851 | 2100 |
| 2020 | Very high human development | 37984 | 51885 | 2939 |
| 2011 | Medium human development | 707 | 1318 | 60 |
| 2012 | Medium human development | 900 | 2034 | 52 |
| 2013 | Medium human development | 736 | 1879 | 65 |
| 2014 | Medium human development | 634 | 1900 | 74 |
| 2015 | Medium human development | 815 | 2376 | 95 |
| 2016 | Medium human development | 961 | 2526 | 96 |
| 2017 | Medium human development | 653 | 2261 | 77 |
| 2018 | Medium human development | 734 | 2382 | 91 |
| 2019 | Medium human development | 916 | 2290 | 80 |
| 2020 | Medium human development | 682 | 1641 | 115 |
| 2011 | Low human development | 29 | 131 | 5 |
| 2012 | Low human development | 42 | 133 | 3 |
| 2013 | Low human development | 13 | 148 | 0 |
| 2014 | Low human development | 10 | 113 | 0 |
| 2015 | Low human development | 30 | 175 | 7 |
| 2016 | Low human development | 53 | 155 | 3 |
| 2017 | Low human development | 56 | 143 | 10 |
| 2018 | Low human development | 140 | 398 | 16 |
| 2019 | Low human development | 149 | 472 | 13 |
| 2020 | Low human development | 28 | 122 | 13 |
| 2011 | High human development | 1568 | 3129 | 136 |
| 2012 | High human development | 1853 | 3793 | 101 |
| 2013 | High human development | 2015 | 4261 | 126 |
| 2014 | High human development | 2017 | 4633 | 138 |
| 2015 | High human development | 2071 | 5250 | 98 |
| 2016 | High human development | 2524 | 4890 | 137 |
| 2017 | High human development | 2460 | 4976 | 186 |
| 2018 | High human development | 2781 | 5443 | 188 |
| 2019 | High human development | 2767 | 6402 | 228 |
| 2020 | High human development | 3122 | 6478 | 296 |
readr::write_csv(t, "../output/1j_hdi_number_references.csv")## j. Appendix - Total number of regerences by HDI categories
p <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, hdi_level) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
tidyr::pivot_longer(cols = -c("Year", "hdi_level"),
names_to = "Type", values_to = "Prop") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_prop" ~ "Climate",
Type == "intersection_prop" ~ "Intersection",
Type == "health_prop" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L),
hdi_level = factor(hdi_level, levels = c("Very high human development",
"High human development",
"Medium human development",
"Low human development"))) %>%
dplyr::filter(Key == "Intersection" & !is.na(hdi_level)) %>%
ggplot(aes(x = Year, y = Prop, color = hdi_level)) +
geom_line() +
theme_minimal() +
labs(y = "Proportion of companies, %",
color = "") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 0.5))
pggsave("../output/1k_hdi_prop_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011) %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, hdi_level) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
dplyr::arrange(desc(hdi_level), Year) %>%
dplyr::filter(!is.na(hdi_level))
colnames(t) <- c("Year", "HDI Level", "Total documents", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | HDI Level | Total documents | Climate | Health | Intersection |
|---|---|---|---|---|---|
| 2011 | Very high human development | 795 | 0.77 | 0.84 | 0.28 |
| 2012 | Very high human development | 1065 | 0.75 | 0.82 | 0.24 |
| 2013 | Very high human development | 1207 | 0.71 | 0.81 | 0.22 |
| 2014 | Very high human development | 1304 | 0.70 | 0.80 | 0.23 |
| 2015 | Very high human development | 1369 | 0.70 | 0.81 | 0.25 |
| 2016 | Very high human development | 1472 | 0.68 | 0.80 | 0.24 |
| 2017 | Very high human development | 1521 | 0.72 | 0.80 | 0.26 |
| 2018 | Very high human development | 1540 | 0.73 | 0.82 | 0.30 |
| 2019 | Very high human development | 1741 | 0.74 | 0.84 | 0.30 |
| 2020 | Very high human development | 1654 | 0.79 | 0.86 | 0.41 |
| 2011 | Medium human development | 101 | 0.52 | 0.78 | 0.21 |
| 2012 | Medium human development | 136 | 0.54 | 0.85 | 0.20 |
| 2013 | Medium human development | 142 | 0.51 | 0.81 | 0.13 |
| 2014 | Medium human development | 148 | 0.51 | 0.80 | 0.11 |
| 2015 | Medium human development | 172 | 0.48 | 0.79 | 0.15 |
| 2016 | Medium human development | 172 | 0.54 | 0.81 | 0.16 |
| 2017 | Medium human development | 173 | 0.53 | 0.81 | 0.15 |
| 2018 | Medium human development | 166 | 0.52 | 0.84 | 0.15 |
| 2019 | Medium human development | 175 | 0.55 | 0.83 | 0.20 |
| 2020 | Medium human development | 123 | 0.54 | 0.84 | 0.20 |
| 2011 | Low human development | 9 | 0.56 | 0.78 | 0.22 |
| 2012 | Low human development | 13 | 0.38 | 0.77 | 0.08 |
| 2013 | Low human development | 24 | 0.17 | 0.71 | 0.00 |
| 2014 | Low human development | 25 | 0.20 | 0.64 | 0.00 |
| 2015 | Low human development | 24 | 0.46 | 0.79 | 0.12 |
| 2016 | Low human development | 28 | 0.36 | 0.68 | 0.11 |
| 2017 | Low human development | 29 | 0.31 | 0.76 | 0.10 |
| 2018 | Low human development | 29 | 0.45 | 0.79 | 0.24 |
| 2019 | Low human development | 39 | 0.46 | 0.79 | 0.15 |
| 2020 | Low human development | 19 | 0.47 | 0.63 | 0.21 |
| 2011 | High human development | 153 | 0.65 | 0.73 | 0.24 |
| 2012 | High human development | 211 | 0.58 | 0.70 | 0.21 |
| 2013 | High human development | 229 | 0.57 | 0.79 | 0.23 |
| 2014 | High human development | 248 | 0.54 | 0.76 | 0.17 |
| 2015 | High human development | 250 | 0.55 | 0.78 | 0.15 |
| 2016 | High human development | 304 | 0.52 | 0.70 | 0.18 |
| 2017 | High human development | 285 | 0.55 | 0.74 | 0.21 |
| 2018 | High human development | 301 | 0.55 | 0.70 | 0.21 |
| 2019 | High human development | 295 | 0.54 | 0.74 | 0.24 |
| 2020 | High human development | 267 | 0.59 | 0.76 | 0.30 |
readr::write_csv(t, "../output/1k_hdi_prop_references.csv")## f. Appendix - Number of references 2020 per sector
t <- total_counts %>%
dplyr::filter(Year == 2020) %>%
dplyr::group_by(Sector, Year) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
dplyr::select(Sector, health_references, climate_references, intersection_references)
colnames(t) <- c("Sector", "Health", "Climate", "Intersection")
readr::write_csv(t, "../output/1l_sector_references_2020.csv")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Health | Climate | Intersection |
|---|---|---|---|
| Aerospace & Defense | 754 | 403 | 46 |
| Alternative Energy | 551 | 866 | 92 |
| Automobiles & Parts | 2032 | 2160 | 105 |
| Banks | 1079 | 1484 | 84 |
| Beverages | 927 | 562 | 37 |
| Chemicals | 3438 | 2078 | 191 |
| Construction & Materials | 3357 | 2699 | 192 |
| Diversified | 1789 | 1024 | 93 |
| Electricity | 1184 | 2041 | 77 |
| Electronic & Electrical Equ… | 1605 | 822 | 64 |
| Equity Investment Instruments | 80 | 99 | 12 |
| Financial Services | 3217 | 4243 | 210 |
| Fixed Line Telecommunications | 327 | 195 | 24 |
| Food & Drug Retailers | 244 | 195 | 16 |
| Food Producers | 2907 | 1282 | 202 |
| Forestry & Paper | 367 | 580 | 22 |
| Gas, Water & Multiutilities | 921 | 821 | 91 |
| General Industrials | 4532 | 2836 | 274 |
| General Retailers | 1470 | 1216 | 40 |
| Health Care Equipment & Ser… | 1751 | 356 | 45 |
| Household Goods & Home Cons… | 1072 | 1022 | 48 |
| Industrial Engineering | 1158 | 767 | 82 |
| Industrial Goods & Services | 12 | 4 | 0 |
| Industrial Metals & Mining | 1358 | 789 | 74 |
| Industrial Transportation | 1275 | 819 | 51 |
| Leisure Goods | 136 | 63 | 2 |
| Life Insurance | 651 | 330 | 41 |
| Media | 680 | 454 | 19 |
| Mining | 1150 | 594 | 55 |
| Mobile Telecommunications | 925 | 629 | 61 |
| Nonequity Investment Instru… | 112 | 37 | 3 |
| Nonlife Insurance | 413 | 260 | 23 |
| Not Applicable | 29 | 16 | 0 |
| Oil & Gas Producers | 1603 | 1490 | 114 |
| Oil Equipment, Services & D… | 594 | 435 | 24 |
| Personal Goods | 806 | 504 | 22 |
| Pharmaceuticals & Biotechno… | 6336 | 1125 | 280 |
| Real Estate Investment & Se… | 1638 | 1289 | 103 |
| Real Estate Investment Trusts | 232 | 241 | 24 |
| Software & Computer Services | 1404 | 1098 | 106 |
| Support Services | 3662 | 2174 | 200 |
| Technology Hardware & Equip… | 1632 | 1131 | 99 |
| Travel & Leisure | 744 | 601 | 17 |
## g. Appendix - Average of references 2020 per sector
t <- total_counts %>%
dplyr::filter(Year == 2020) %>%
dplyr::group_by(Sector, Year) %>%
dplyr::summarize(climate_references = mean(climate_count, na.rm = T),
health_references = mean(health_count, na.rm = T),
intersection_references = mean(intersection_count, na.rm = T)
) %>%
dplyr::mutate_if(is.numeric,round,1) %>%
dplyr::select(Sector, health_references, climate_references, intersection_references)
colnames(t) <-c("Sector", "Health, %", "Climate, %", "Intersection, %")
readr::write_csv(t, "../output/1m_avg_sector_references_2020.csv")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Health, % | Climate, % | Intersection, % |
|---|---|---|---|
| Aerospace & Defense | 47.1 | 25.2 | 2.9 |
| Alternative Energy | 24.0 | 37.7 | 4.0 |
| Automobiles & Parts | 48.4 | 51.4 | 2.5 |
| Banks | 21.6 | 29.7 | 1.7 |
| Beverages | 29.0 | 17.6 | 1.2 |
| Chemicals | 58.3 | 35.2 | 3.2 |
| Construction & Materials | 41.4 | 33.3 | 2.4 |
| Diversified | 19.7 | 11.3 | 1.0 |
| Electricity | 34.8 | 60.0 | 2.3 |
| Electronic & Electrical Equ… | 26.8 | 13.7 | 1.1 |
| Equity Investment Instruments | 8.9 | 11.0 | 1.3 |
| Financial Services | 24.4 | 32.1 | 1.6 |
| Fixed Line Telecommunications | 27.2 | 16.2 | 2.0 |
| Food & Drug Retailers | 61.0 | 48.8 | 4.0 |
| Food Producers | 37.3 | 16.4 | 2.6 |
| Forestry & Paper | 30.6 | 48.3 | 1.8 |
| Gas, Water & Multiutilities | 41.9 | 37.3 | 4.1 |
| General Industrials | 32.4 | 20.3 | 2.0 |
| General Retailers | 28.3 | 23.4 | 0.8 |
| Health Care Equipment & Ser… | 42.7 | 8.7 | 1.1 |
| Household Goods & Home Cons… | 35.7 | 34.1 | 1.6 |
| Industrial Engineering | 30.5 | 20.2 | 2.2 |
| Industrial Goods & Services | 6.0 | 2.0 | 0.0 |
| Industrial Metals & Mining | 52.2 | 30.3 | 2.8 |
| Industrial Transportation | 28.3 | 18.2 | 1.1 |
| Leisure Goods | 15.1 | 7.0 | 0.2 |
| Life Insurance | 65.1 | 33.0 | 4.1 |
| Media | 18.9 | 12.6 | 0.5 |
| Mining | 63.9 | 33.0 | 3.1 |
| Mobile Telecommunications | 37.0 | 25.2 | 2.4 |
| Nonequity Investment Instru… | 56.0 | 18.5 | 1.5 |
| Nonlife Insurance | 31.8 | 20.0 | 1.8 |
| Not Applicable | 29.0 | 16.0 | 0.0 |
| Oil & Gas Producers | 53.4 | 49.7 | 3.8 |
| Oil Equipment, Services & D… | 24.8 | 18.1 | 1.0 |
| Personal Goods | 17.5 | 11.0 | 0.5 |
| Pharmaceuticals & Biotechno… | 124.2 | 22.1 | 5.5 |
| Real Estate Investment & Se… | 35.6 | 28.0 | 2.2 |
| Real Estate Investment Trusts | 29.0 | 30.1 | 3.0 |
| Software & Computer Services | 15.4 | 12.1 | 1.2 |
| Support Services | 18.3 | 10.9 | 1.0 |
| Technology Hardware & Equip… | 32.6 | 22.6 | 2.0 |
| Travel & Leisure | 22.5 | 18.2 | 0.5 |
## h. Appendix - Proportion of companies 2020 per sector
p <- plot_df %>%
dplyr::filter(Year >= "2020-01-01") %>%
ggplot(., aes(x = Sector, y = Prop, fill = Key)) +
geom_bar(stat = "identity", position = "dodge") +
theme_minimal() +
scale_y_continuous(labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
theme(legend.position = "bottom") +
labs(fill = "",
y = "Proportion of companies, %",
x = "\nSector")
pggsave("../output/1n_prop_companies_sector.pdf", p, width = 10, height = 7)t <- plot_df %>%
dplyr::filter(Year >= "2020-01-01") %>%
dplyr::select(Sector, Year, Key, Prop) %>%
dplyr::mutate(Year = lubridate::year(Year),
Prop = round(Prop, 2)) %>%
dplyr::arrange(Sector, Year) %>%
tidyr::pivot_wider(names_from = "Key", values_from = "Prop")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Year | Climate | Health | Intersection |
|---|---|---|---|---|
| Aerospace & Defense | 2020 | 0.72 | 0.89 | 0.39 |
| Alternative Energy | 2020 | 0.91 | 0.96 | 0.70 |
| Automobiles & Parts | 2020 | 0.83 | 0.89 | 0.55 |
| Banks | 2020 | 0.87 | 0.85 | 0.42 |
| Beverages | 2020 | 0.61 | 0.86 | 0.33 |
| Chemicals | 2020 | 0.85 | 0.89 | 0.68 |
| Construction & Materials | 2020 | 0.78 | 0.83 | 0.44 |
| Diversified | 2020 | 0.78 | 0.84 | 0.31 |
| Electricity | 2020 | 0.79 | 0.79 | 0.51 |
| Electronic & Electrical Equ… | 2020 | 0.66 | 0.85 | 0.29 |
| Equity Investment Instruments | 2020 | 0.60 | 0.90 | 0.30 |
| Financial Services | 2020 | 0.80 | 0.77 | 0.37 |
| Fixed Line Telecommunications | 2020 | 0.69 | 0.85 | 0.38 |
| Food & Drug Retailers | 2020 | 1.00 | 1.00 | 0.75 |
| Food Producers | 2020 | 0.76 | 0.89 | 0.47 |
| Forestry & Paper | 2020 | 0.71 | 0.86 | 0.36 |
| Gas, Water & Multiutilities | 2020 | 0.76 | 0.88 | 0.60 |
| General Industrials | 2020 | 0.77 | 0.86 | 0.39 |
| General Retailers | 2020 | 0.71 | 0.81 | 0.31 |
| Health Care Equipment & Ser… | 2020 | 0.70 | 0.89 | 0.37 |
| Household Goods & Home Cons… | 2020 | 0.74 | 0.94 | 0.45 |
| Industrial Engineering | 2020 | 0.67 | 0.73 | 0.33 |
| Industrial Goods & Services | 2020 | 0.50 | 1.00 | 0.00 |
| Industrial Metals & Mining | 2020 | 0.89 | 0.89 | 0.46 |
| Industrial Transportation | 2020 | 0.73 | 0.81 | 0.33 |
| Leisure Goods | 2020 | 0.64 | 0.73 | 0.18 |
| Life Insurance | 2020 | 0.91 | 0.64 | 0.36 |
| Media | 2020 | 0.73 | 0.75 | 0.14 |
| Mining | 2020 | 0.89 | 0.89 | 0.58 |
| Mobile Telecommunications | 2020 | 0.81 | 0.96 | 0.50 |
| Nonequity Investment Instru… | 2020 | 0.50 | 1.00 | 0.50 |
| Nonlife Insurance | 2020 | 0.77 | 1.00 | 0.38 |
| Not Applicable | 2020 | 1.00 | 1.00 | 0.00 |
| Oil & Gas Producers | 2020 | 0.84 | 0.91 | 0.72 |
| Oil Equipment, Services & D… | 2020 | 0.67 | 0.89 | 0.37 |
| Personal Goods | 2020 | 0.70 | 0.81 | 0.22 |
| Pharmaceuticals & Biotechno… | 2020 | 0.82 | 0.93 | 0.60 |
| Real Estate Investment & Se… | 2020 | 0.78 | 0.92 | 0.50 |
| Real Estate Investment Trusts | 2020 | 0.70 | 0.80 | 0.60 |
| Software & Computer Services | 2020 | 0.63 | 0.83 | 0.25 |
| Support Services | 2020 | 0.66 | 0.81 | 0.27 |
| Technology Hardware & Equip… | 2020 | 0.83 | 0.85 | 0.36 |
| Travel & Leisure | 2020 | 0.76 | 0.79 | 0.16 |
readr::write_csv(t, "../output/1n_prop_companies_sector.csv")intersection_covid %>% knitr::kable(col.names = c("Year", "Total documents", "Keyword hits", "Flagged documents (N)", "Flagged documents (Prop)")) %>%
kableExtra::add_header_above(c("", "", "COVID dictionary" = 3)) %>%
kableExtra::kable_styling()| Year | Total documents | Keyword hits | Flagged documents (N) | Flagged documents (Prop) |
|---|---|---|---|---|
| 2020 | 791 | 128 | 113 | 0.1428571 |
intersection_gender %>% knitr::kable(col.names = c("Year", "Total documents", "Keyword hits", "Flagged documents (N)", "Flagged documents (Prop)")) %>%
kableExtra::add_header_above(c("", "", "Gender dictionary" = 3)) %>%
kableExtra::kable_styling()| Year | Total documents | Keyword hits | Flagged documents (N) | Flagged documents (Prop) |
|---|---|---|---|---|
| 2011 | 282 | 26 | 24 | 0.09 |
| 2012 | 327 | 20 | 17 | 0.05 |
| 2013 | 343 | 29 | 21 | 0.06 |
| 2014 | 357 | 26 | 17 | 0.05 |
| 2015 | 408 | 37 | 27 | 0.07 |
| 2016 | 440 | 51 | 40 | 0.09 |
| 2017 | 484 | 57 | 48 | 0.10 |
| 2018 | 553 | 71 | 59 | 0.11 |
| 2019 | 637 | 151 | 123 | 0.19 |
| 2020 | 791 | 113 | 99 | 0.13 |
readr::write_csv(intersection_covid, "../output/1o_intersection_covid.csv")
readr::write_csv(intersection_gender, "../output/1o_intersection_gender.csv")
p <- ggplot(intersection_gender, aes(x = year, y = prop_doct, group = 1)) +
geom_line(color = "#cc0055") +
theme_minimal() +
labs(x= "Year",
y = "Proportion of documents\n Gender mention in intersection, %\n") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 0.25))
pggsave("../output/1o_prop_intersecion_gender.pdf", p, width = 10, height = 7)## a. Main text - Proportion of companies, %
p <- plot_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser...") %>%
ggplot(aes(x = Year, y = Prop, color = Key)) +
geom_line(aes(linetype = Key)) +
scale_linetype_manual(values = c("solid", "longdash", "twodash")) +
ggplot2::annotate("text", x = as.Date("2013-05-31"), y = 0.85, label = "Health", color = "#619cff") +
ggplot2::annotate("text", x = as.Date("2013-05-31"), y = 0.48, label = "Climate Change", color = "darkgreen") +
ggplot2::annotate("text", x = as.Date("2012-12-31"), y = 0.1, label = "Intersection", color = "red") +
scale_y_continuous(labels = scales::percent_format()) +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Proportion of companies, %")
pggsave("../output/2p_prop_of_companies.pdf", p, width = 10, height = 7)t <- plot_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser...") %>%
dplyr::select(Year, Key, Prop) %>%
dplyr::mutate(Prop = round(Prop, 2),
Year = lubridate::year(Year)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "Prop")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Climate | Health | Intersection |
|---|---|---|---|
| 2011 | 0.57 | 1.00 | 0.00 |
| 2012 | 0.50 | 0.89 | 0.22 |
| 2013 | 0.34 | 0.78 | 0.16 |
| 2014 | 0.33 | 0.80 | 0.13 |
| 2015 | 0.47 | 0.87 | 0.17 |
| 2016 | 0.50 | 0.78 | 0.19 |
| 2017 | 0.56 | 0.85 | 0.09 |
| 2018 | 0.73 | 0.89 | 0.35 |
| 2019 | 0.69 | 0.95 | 0.31 |
| 2020 | 0.70 | 0.89 | 0.37 |
readr::write_csv(t, "../output/2p_prop_of_companies.csv")## b. Appendix - Total number of references
p <- reference_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser...") %>%
ggplot(aes(x = Year, y = Count, color = Key)) +
geom_line(aes(linetype = Key)) +
scale_linetype_manual(values = c("solid", "longdash", "twodash")) +
ggplot2::annotate("text", x = as.Date("2011-05-31"), y = 500, label = "Health", color = "#619cff") +
ggplot2::annotate("text", x = as.Date("2013-05-31"), y = 250, label = "Climate Change", color = "darkgreen") +
ggplot2::annotate("text", x = as.Date("2016-12-31"), y = 75, label = "Intersection", color = "red") +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Total number of references")
pggsave("../output/2q_number_of_references.pdf", p, width = 10, height = 7)t <- reference_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser...") %>%
dplyr::select(Year, Key, Count) %>%
dplyr::mutate(Year = lubridate::year(Year)) %>%
tidyr::pivot_wider(id_cols = Year, names_from = "Key", values_from = "Count")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Climate | Health | Intersection |
|---|---|---|---|
| 2011 | 90 | 377 | 0 |
| 2012 | 82 | 478 | 7 |
| 2013 | 139 | 908 | 40 |
| 2014 | 152 | 1248 | 66 |
| 2015 | 195 | 1277 | 27 |
| 2016 | 162 | 891 | 27 |
| 2017 | 279 | 1112 | 5 |
| 2018 | 314 | 1870 | 48 |
| 2019 | 352 | 1979 | 45 |
| 2020 | 356 | 1751 | 45 |
readr::write_csv(t, "../output/2q_number_of_references.csv")## c. Appendix - Total number of references (Intersection)
p <- reference_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser..." & Key == "Intersection") %>%
ggplot(aes(x = Year, y = Count, color = Key)) +
geom_line() +
ggplot2::annotate("text", x = as.Date("2013-12-31"), y = 18, label = "Intersection", color = "red") +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Total number of references")
pggsave("../output/2r_number_of_references_intersection.pdf", p, width = 10, height = 7)t <- reference_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser..." & Key == "Intersection") %>%
dplyr::mutate(Year = lubridate::year(Year)) %>%
dplyr::select(Sector, Year, Count)
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Year | Count |
|---|---|---|
| Health Care Equipment & Ser… | 2011 | 0 |
| Health Care Equipment & Ser… | 2012 | 7 |
| Health Care Equipment & Ser… | 2013 | 40 |
| Health Care Equipment & Ser… | 2014 | 66 |
| Health Care Equipment & Ser… | 2015 | 27 |
| Health Care Equipment & Ser… | 2016 | 27 |
| Health Care Equipment & Ser… | 2017 | 5 |
| Health Care Equipment & Ser… | 2018 | 48 |
| Health Care Equipment & Ser… | 2019 | 45 |
| Health Care Equipment & Ser… | 2020 | 45 |
readr::write_csv(t, "../output/2r_number_of_references_intersection.csv")## d. Appendix - Average number of references
p <- reference_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser...") %>%
ggplot(aes(x = Year, y = Avg, color = Key)) +
geom_line(aes(linetype = Key)) +
scale_linetype_manual(values = c("solid", "longdash", "twodash")) +
ggplot2::annotate("text", x = as.Date("2011-05-31"), y = 32, label = "Health", color = "#619cff") +
ggplot2::annotate("text", x = as.Date("2012-05-31"), y = 8, label = "Climate Change", color = "darkgreen") +
ggplot2::annotate("text", x = as.Date("2016-12-31"), y = 3, label = "Intersection", color = "red") +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Average number of references")
pggsave("../output/2s_avg_references.pdf", p, width = 10, height = 7)t <- reference_df %>%
dplyr::filter(Sector == "Health Care Equipment & Ser..." & Key == "Intersection") %>%
dplyr::mutate(Year = lubridate::year(Year),
Avg = round(Avg, 2)) %>%
dplyr::select(Sector, Year, Avg)
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Year | Avg |
|---|---|---|
| Health Care Equipment & Ser… | 2011 | 0.00 |
| Health Care Equipment & Ser… | 2012 | 0.44 |
| Health Care Equipment & Ser… | 2013 | 1.60 |
| Health Care Equipment & Ser… | 2014 | 2.75 |
| Health Care Equipment & Ser… | 2015 | 1.04 |
| Health Care Equipment & Ser… | 2016 | 1.00 |
| Health Care Equipment & Ser… | 2017 | 0.17 |
| Health Care Equipment & Ser… | 2018 | 1.37 |
| Health Care Equipment & Ser… | 2019 | 1.12 |
| Health Care Equipment & Ser… | 2020 | 1.10 |
readr::write_csv(t, "../output/2s_avg_references.csv")## e. Appendix - Total number of references by WHO region
p <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::group_by(Sector, Year, who_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Sector", "Year", "who_region"),
names_to = "Type", values_to = "Count") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection") %>%
ggplot(aes(x = Year, y = Count, color = who_region)) +
geom_path() +
theme_minimal() +
labs(y = "Total number of references",
color = "WHO Region")
pggsave("../output/2t_who_number_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::group_by(Sector, Year, who_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
dplyr::arrange(who_region, Year)
colnames(t) <- c("Sector", "Year", "WHO Region", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Year | WHO Region | Climate | Health | Intersection |
|---|---|---|---|---|---|
| Health Care Equipment & Ser… | 2011 | Africa | 1 | 48 | 0 |
| Health Care Equipment & Ser… | 2012 | Africa | 3 | 115 | 1 |
| Health Care Equipment & Ser… | 2013 | Africa | 0 | 86 | 0 |
| Health Care Equipment & Ser… | 2014 | Africa | 24 | 201 | 44 |
| Health Care Equipment & Ser… | 2015 | Africa | 32 | 175 | 12 |
| Health Care Equipment & Ser… | 2016 | Africa | 2 | 53 | 2 |
| Health Care Equipment & Ser… | 2017 | Africa | 0 | 9 | 0 |
| Health Care Equipment & Ser… | 2018 | Africa | 8 | 107 | 1 |
| Health Care Equipment & Ser… | 2019 | Africa | 33 | 144 | 3 |
| Health Care Equipment & Ser… | 2020 | Africa | 19 | 68 | 2 |
| Health Care Equipment & Ser… | 2011 | Eastern Mediterranean | 0 | 12 | 0 |
| Health Care Equipment & Ser… | 2012 | Eastern Mediterranean | 0 | 10 | 0 |
| Health Care Equipment & Ser… | 2013 | Eastern Mediterranean | 9 | 195 | 0 |
| Health Care Equipment & Ser… | 2014 | Eastern Mediterranean | 0 | 2 | 0 |
| Health Care Equipment & Ser… | 2015 | Eastern Mediterranean | 0 | 3 | 0 |
| Health Care Equipment & Ser… | 2019 | Eastern Mediterranean | 0 | 57 | 0 |
| Health Care Equipment & Ser… | 2020 | Eastern Mediterranean | 0 | 6 | 0 |
| Health Care Equipment & Ser… | 2011 | Europe | 19 | 67 | 0 |
| Health Care Equipment & Ser… | 2012 | Europe | 28 | 114 | 0 |
| Health Care Equipment & Ser… | 2013 | Europe | 49 | 245 | 8 |
| Health Care Equipment & Ser… | 2014 | Europe | 71 | 608 | 10 |
| Health Care Equipment & Ser… | 2015 | Europe | 87 | 436 | 4 |
| Health Care Equipment & Ser… | 2016 | Europe | 78 | 455 | 4 |
| Health Care Equipment & Ser… | 2017 | Europe | 127 | 519 | 1 |
| Health Care Equipment & Ser… | 2018 | Europe | 157 | 1026 | 25 |
| Health Care Equipment & Ser… | 2019 | Europe | 189 | 760 | 21 |
| Health Care Equipment & Ser… | 2020 | Europe | 220 | 1016 | 34 |
| Health Care Equipment & Ser… | 2012 | Latin America and the Caribbean | 15 | 117 | 3 |
| Health Care Equipment & Ser… | 2013 | Latin America and the Caribbean | 5 | 122 | 1 |
| Health Care Equipment & Ser… | 2014 | Latin America and the Caribbean | 0 | 0 | 0 |
| Health Care Equipment & Ser… | 2015 | Latin America and the Caribbean | 3 | 163 | 0 |
| Health Care Equipment & Ser… | 2016 | Latin America and the Caribbean | 0 | 0 | 0 |
| Health Care Equipment & Ser… | 2017 | Latin America and the Caribbean | 0 | 0 | 0 |
| Health Care Equipment & Ser… | 2018 | Latin America and the Caribbean | 3 | 10 | 0 |
| Health Care Equipment & Ser… | 2019 | Latin America and the Caribbean | 7 | 267 | 0 |
| Health Care Equipment & Ser… | 2020 | Latin America and the Caribbean | 3 | 187 | 0 |
| Health Care Equipment & Ser… | 2011 | North America | 2 | 8 | 0 |
| Health Care Equipment & Ser… | 2012 | North America | 2 | 8 | 1 |
| Health Care Equipment & Ser… | 2013 | North America | 0 | 10 | 0 |
| Health Care Equipment & Ser… | 2014 | North America | 0 | 0 | 0 |
| Health Care Equipment & Ser… | 2017 | North America | 29 | 42 | 2 |
| Health Care Equipment & Ser… | 2018 | North America | 25 | 70 | 10 |
| Health Care Equipment & Ser… | 2019 | North America | 35 | 447 | 13 |
| Health Care Equipment & Ser… | 2020 | North America | 19 | 97 | 1 |
| Health Care Equipment & Ser… | 2011 | South-East Asia | 2 | 44 | 0 |
| Health Care Equipment & Ser… | 2012 | South-East Asia | 0 | 4 | 0 |
| Health Care Equipment & Ser… | 2013 | South-East Asia | 0 | 0 | 0 |
| Health Care Equipment & Ser… | 2014 | South-East Asia | 0 | 112 | 0 |
| Health Care Equipment & Ser… | 2015 | South-East Asia | 0 | 170 | 0 |
| Health Care Equipment & Ser… | 2016 | South-East Asia | 10 | 43 | 0 |
| Health Care Equipment & Ser… | 2017 | South-East Asia | 10 | 47 | 0 |
| Health Care Equipment & Ser… | 2018 | South-East Asia | 10 | 46 | 0 |
| Health Care Equipment & Ser… | 2019 | South-East Asia | 10 | 61 | 0 |
| Health Care Equipment & Ser… | 2020 | South-East Asia | 6 | 8 | 2 |
| Health Care Equipment & Ser… | 2011 | Western Pacific | 66 | 198 | 0 |
| Health Care Equipment & Ser… | 2012 | Western Pacific | 34 | 110 | 2 |
| Health Care Equipment & Ser… | 2013 | Western Pacific | 76 | 250 | 31 |
| Health Care Equipment & Ser… | 2014 | Western Pacific | 57 | 325 | 12 |
| Health Care Equipment & Ser… | 2015 | Western Pacific | 73 | 330 | 11 |
| Health Care Equipment & Ser… | 2016 | Western Pacific | 72 | 340 | 21 |
| Health Care Equipment & Ser… | 2017 | Western Pacific | 113 | 495 | 2 |
| Health Care Equipment & Ser… | 2018 | Western Pacific | 111 | 611 | 12 |
| Health Care Equipment & Ser… | 2019 | Western Pacific | 78 | 243 | 8 |
| Health Care Equipment & Ser… | 2020 | Western Pacific | 89 | 369 | 6 |
readr::write_csv(t, "../output/2t_who_number_references.csv")p <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, who_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
tidyr::pivot_longer(cols = -c("Year", "who_region"),
names_to = "Type", values_to = "Prop") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_prop" ~ "Climate",
Type == "intersection_prop" ~ "Intersection",
Type == "health_prop" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection" & !is.na(who_region)) %>%
ggplot(aes(x = Year, y = Prop, color = who_region)) +
geom_line() +
theme_minimal() +
labs(y = "Proportion of companies, %",
color = "") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 1))
pggsave("../output/2u_who_prop_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, who_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
dplyr::arrange(desc(who_region), Year) %>%
dplyr::filter(!is.na(who_region))
colnames(t) <- c("Year", "WHO Region", "Total documents", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | WHO Region | Total documents | Climate | Health | Intersection |
|---|---|---|---|---|---|
| 2011 | Western Pacific | 3 | 1.00 | 1.00 | 0.00 |
| 2012 | Western Pacific | 1 | 1.00 | 1.00 | 1.00 |
| 2013 | Western Pacific | 5 | 0.80 | 0.80 | 0.40 |
| 2014 | Western Pacific | 3 | 0.67 | 0.67 | 0.33 |
| 2015 | Western Pacific | 2 | 1.00 | 1.00 | 0.50 |
| 2016 | Western Pacific | 5 | 1.00 | 0.80 | 0.60 |
| 2017 | Western Pacific | 5 | 0.60 | 0.60 | 0.20 |
| 2018 | Western Pacific | 5 | 1.00 | 1.00 | 0.40 |
| 2019 | Western Pacific | 5 | 0.80 | 0.80 | 0.40 |
| 2020 | Western Pacific | 4 | 1.00 | 1.00 | 0.25 |
| 2011 | South-East Asia | 2 | 0.50 | 1.00 | 0.00 |
| 2012 | South-East Asia | 1 | 0.00 | 1.00 | 0.00 |
| 2013 | South-East Asia | 1 | 0.00 | 0.00 | 0.00 |
| 2014 | South-East Asia | 2 | 0.00 | 1.00 | 0.00 |
| 2015 | South-East Asia | 2 | 0.00 | 1.00 | 0.00 |
| 2016 | South-East Asia | 2 | 0.50 | 1.00 | 0.00 |
| 2017 | South-East Asia | 2 | 0.50 | 1.00 | 0.00 |
| 2018 | South-East Asia | 2 | 0.50 | 1.00 | 0.00 |
| 2019 | South-East Asia | 2 | 0.50 | 1.00 | 0.00 |
| 2020 | South-East Asia | 2 | 1.00 | 1.00 | 0.50 |
| 2011 | North America | 1 | 1.00 | 1.00 | 0.00 |
| 2012 | North America | 2 | 0.50 | 1.00 | 0.50 |
| 2013 | North America | 3 | 0.00 | 0.67 | 0.00 |
| 2014 | North America | 1 | 0.00 | 0.00 | 0.00 |
| 2017 | North America | 1 | 1.00 | 1.00 | 1.00 |
| 2018 | North America | 2 | 1.00 | 1.00 | 1.00 |
| 2019 | North America | 3 | 1.00 | 1.00 | 0.67 |
| 2020 | North America | 4 | 1.00 | 1.00 | 0.25 |
| 2012 | Latin America and the Caribbean | 2 | 0.50 | 0.50 | 0.50 |
| 2013 | Latin America and the Caribbean | 3 | 0.33 | 0.33 | 0.33 |
| 2014 | Latin America and the Caribbean | 1 | 0.00 | 0.00 | 0.00 |
| 2015 | Latin America and the Caribbean | 5 | 0.20 | 0.40 | 0.00 |
| 2016 | Latin America and the Caribbean | 2 | 0.00 | 0.00 | 0.00 |
| 2017 | Latin America and the Caribbean | 2 | 0.00 | 0.00 | 0.00 |
| 2018 | Latin America and the Caribbean | 4 | 0.50 | 0.25 | 0.00 |
| 2019 | Latin America and the Caribbean | 3 | 0.67 | 0.67 | 0.00 |
| 2020 | Latin America and the Caribbean | 3 | 0.67 | 0.67 | 0.00 |
| 2011 | Europe | 5 | 0.40 | 1.00 | 0.00 |
| 2012 | Europe | 10 | 0.50 | 0.90 | 0.00 |
| 2013 | Europe | 16 | 0.31 | 0.88 | 0.12 |
| 2014 | Europe | 18 | 0.39 | 0.83 | 0.11 |
| 2015 | Europe | 15 | 0.60 | 0.93 | 0.20 |
| 2016 | Europe | 19 | 0.47 | 0.79 | 0.11 |
| 2017 | Europe | 21 | 0.67 | 0.95 | 0.05 |
| 2018 | Europe | 20 | 0.80 | 0.95 | 0.40 |
| 2019 | Europe | 22 | 0.82 | 1.00 | 0.36 |
| 2020 | Europe | 29 | 0.66 | 0.86 | 0.45 |
| 2011 | Eastern Mediterranean | 2 | 0.00 | 1.00 | 0.00 |
| 2012 | Eastern Mediterranean | 1 | 0.00 | 1.00 | 0.00 |
| 2013 | Eastern Mediterranean | 3 | 0.33 | 1.00 | 0.00 |
| 2014 | Eastern Mediterranean | 1 | 0.00 | 1.00 | 0.00 |
| 2015 | Eastern Mediterranean | 1 | 0.00 | 1.00 | 0.00 |
| 2019 | Eastern Mediterranean | 1 | 0.00 | 1.00 | 0.00 |
| 2020 | Eastern Mediterranean | 1 | 0.00 | 1.00 | 0.00 |
| 2011 | Africa | 1 | 1.00 | 1.00 | 0.00 |
| 2012 | Africa | 1 | 1.00 | 1.00 | 1.00 |
| 2013 | Africa | 1 | 0.00 | 1.00 | 0.00 |
| 2014 | Africa | 4 | 0.25 | 1.00 | 0.25 |
| 2015 | Africa | 5 | 0.40 | 1.00 | 0.20 |
| 2016 | Africa | 4 | 0.25 | 1.00 | 0.25 |
| 2017 | Africa | 3 | 0.00 | 1.00 | 0.00 |
| 2018 | Africa | 4 | 0.25 | 1.00 | 0.25 |
| 2019 | Africa | 6 | 0.17 | 1.00 | 0.17 |
| 2020 | Africa | 3 | 0.33 | 1.00 | 0.33 |
readr::write_csv(t, "../output/2u_who_prop_references.csv")## i. Appendix - Total number of regerences by SIDS, Tier 1 and Tier 2
p <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::group_by(Sector, Year, tier_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Sector", "Year", "tier_region"),
names_to = "Type", values_to = "Count") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection" & !is.na(tier_region)) %>%
ggplot(aes(x = Year, y = Count, color = tier_region)) +
geom_path() +
theme_minimal() +
labs(y = "Total number of references",
color = "")
pggsave("../output/2v_sids_number_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::group_by(Sector, Year, tier_region) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
dplyr::filter(!is.na(tier_region)) %>%
dplyr::arrange(tier_region, Year)
colnames(t) <- c("Sector", "Year", "Region", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Year | Region | Climate | Health | Intersection |
|---|---|---|---|---|---|
| Health Care Equipment & Ser… | 2018 | SIDS | 1 | 8 | 0 |
| Health Care Equipment & Ser… | 2019 | SIDS | 4 | 10 | 2 |
| Health Care Equipment & Ser… | 2011 | Tier 1 | 2 | 8 | 0 |
| Health Care Equipment & Ser… | 2012 | Tier 1 | 2 | 8 | 1 |
| Health Care Equipment & Ser… | 2013 | Tier 1 | 2 | 16 | 0 |
| Health Care Equipment & Ser… | 2014 | Tier 1 | 0 | 0 | 0 |
| Health Care Equipment & Ser… | 2016 | Tier 1 | 2 | 0 | 0 |
| Health Care Equipment & Ser… | 2017 | Tier 1 | 29 | 42 | 2 |
| Health Care Equipment & Ser… | 2018 | Tier 1 | 25 | 70 | 10 |
| Health Care Equipment & Ser… | 2019 | Tier 1 | 35 | 447 | 13 |
| Health Care Equipment & Ser… | 2020 | Tier 1 | 19 | 97 | 1 |
| Health Care Equipment & Ser… | 2011 | Tier 2 | 19 | 44 | 0 |
| Health Care Equipment & Ser… | 2012 | Tier 2 | 35 | 180 | 3 |
| Health Care Equipment & Ser… | 2013 | Tier 2 | 39 | 234 | 2 |
| Health Care Equipment & Ser… | 2014 | Tier 2 | 49 | 428 | 10 |
| Health Care Equipment & Ser… | 2015 | Tier 2 | 55 | 353 | 1 |
| Health Care Equipment & Ser… | 2016 | Tier 2 | 44 | 197 | 2 |
| Health Care Equipment & Ser… | 2017 | Tier 2 | 39 | 197 | 0 |
| Health Care Equipment & Ser… | 2018 | Tier 2 | 69 | 420 | 21 |
| Health Care Equipment & Ser… | 2019 | Tier 2 | 120 | 543 | 14 |
| Health Care Equipment & Ser… | 2020 | Tier 2 | 42 | 521 | 14 |
readr::write_csv(t, "../output/2v_sids_number_references.csv")p <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, tier_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
tidyr::pivot_longer(cols = -c("Year", "tier_region"),
names_to = "Type", values_to = "Prop") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_prop" ~ "Climate",
Type == "intersection_prop" ~ "Intersection",
Type == "health_prop" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L)) %>%
dplyr::filter(Key == "Intersection" & !is.na(tier_region)) %>%
ggplot(aes(x = Year, y = Prop, color = tier_region)) +
geom_line() +
theme_minimal() +
labs(y = "Proportion of companies, %",
color = "") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 1))
pggsave("../output/2w_sids_prop_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, tier_region) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
dplyr::arrange(desc(tier_region), Year) %>%
dplyr::filter(!is.na(tier_region))
colnames(t) <- c("Year", "Category", "Total documents", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | Category | Total documents | Climate | Health | Intersection |
|---|---|---|---|---|---|
| 2011 | Tier 2 | 3 | 0.67 | 1.00 | 0.00 |
| 2012 | Tier 2 | 6 | 0.67 | 0.83 | 0.17 |
| 2013 | Tier 2 | 11 | 0.36 | 0.64 | 0.18 |
| 2014 | Tier 2 | 10 | 0.50 | 0.80 | 0.20 |
| 2015 | Tier 2 | 12 | 0.50 | 0.75 | 0.08 |
| 2016 | Tier 2 | 8 | 0.50 | 0.50 | 0.12 |
| 2017 | Tier 2 | 9 | 0.44 | 0.89 | 0.00 |
| 2018 | Tier 2 | 8 | 0.88 | 0.88 | 0.62 |
| 2019 | Tier 2 | 11 | 0.91 | 1.00 | 0.36 |
| 2020 | Tier 2 | 12 | 0.67 | 0.83 | 0.25 |
| 2011 | Tier 1 | 1 | 1.00 | 1.00 | 0.00 |
| 2012 | Tier 1 | 2 | 0.50 | 1.00 | 0.50 |
| 2013 | Tier 1 | 6 | 0.33 | 0.67 | 0.00 |
| 2014 | Tier 1 | 2 | 0.00 | 0.00 | 0.00 |
| 2016 | Tier 1 | 1 | 1.00 | 0.00 | 0.00 |
| 2017 | Tier 1 | 1 | 1.00 | 1.00 | 1.00 |
| 2018 | Tier 1 | 2 | 1.00 | 1.00 | 1.00 |
| 2019 | Tier 1 | 3 | 1.00 | 1.00 | 0.67 |
| 2020 | Tier 1 | 4 | 1.00 | 1.00 | 0.25 |
| 2018 | SIDS | 1 | 1.00 | 1.00 | 0.00 |
| 2019 | SIDS | 1 | 1.00 | 1.00 | 1.00 |
readr::write_csv(t, "../output/2w_sids_prop_references.csv")## j. Appendix - Total number of regerences by HDI categories
p <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::group_by(Sector, Year, hdi_level) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
tidyr::pivot_longer(cols = -c("Sector", "Year", "hdi_level"),
names_to = "Type", values_to = "Count") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_references" ~ "Climate",
Type == "intersection_references" ~ "Intersection",
Type == "health_references" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L),
hdi_level = factor(hdi_level, levels = c("Very high human development",
"High human development",
"Medium human development",
"Low human development"))) %>%
dplyr::filter(Key == "Intersection" & !is.na(hdi_level)) %>%
ggplot(aes(x = Year, y = Count, color = hdi_level)) +
geom_path() +
theme_minimal() +
labs(y = "Total number of references",
color = "")
pggsave("../output/2x_hdi_number_references.pdf", p, width = 10, height = 7)total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::group_by(Sector, Year, hdi_level) %>%
dplyr::summarize(climate_references = sum(climate_count, na.rm = T),
health_references = sum(health_count, na.rm = T),
intersection_references = sum(intersection_count, na.rm = T)
) %>%
dplyr::filter(!is.na(hdi_level)) %>%
dplyr::arrange(hdi_level, Year) colnames(t) <- c("Sector", "Year", "HDI Level", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Sector | Year | HDI Level | Climate | Health | Intersection |
|---|---|---|---|---|---|
| 2011 | Tier 2 | 3 | 0.67 | 1.00 | 0.00 |
| 2012 | Tier 2 | 6 | 0.67 | 0.83 | 0.17 |
| 2013 | Tier 2 | 11 | 0.36 | 0.64 | 0.18 |
| 2014 | Tier 2 | 10 | 0.50 | 0.80 | 0.20 |
| 2015 | Tier 2 | 12 | 0.50 | 0.75 | 0.08 |
| 2016 | Tier 2 | 8 | 0.50 | 0.50 | 0.12 |
| 2017 | Tier 2 | 9 | 0.44 | 0.89 | 0.00 |
| 2018 | Tier 2 | 8 | 0.88 | 0.88 | 0.62 |
| 2019 | Tier 2 | 11 | 0.91 | 1.00 | 0.36 |
| 2020 | Tier 2 | 12 | 0.67 | 0.83 | 0.25 |
| 2011 | Tier 1 | 1 | 1.00 | 1.00 | 0.00 |
| 2012 | Tier 1 | 2 | 0.50 | 1.00 | 0.50 |
| 2013 | Tier 1 | 6 | 0.33 | 0.67 | 0.00 |
| 2014 | Tier 1 | 2 | 0.00 | 0.00 | 0.00 |
| 2016 | Tier 1 | 1 | 1.00 | 0.00 | 0.00 |
| 2017 | Tier 1 | 1 | 1.00 | 1.00 | 1.00 |
| 2018 | Tier 1 | 2 | 1.00 | 1.00 | 1.00 |
| 2019 | Tier 1 | 3 | 1.00 | 1.00 | 0.67 |
| 2020 | Tier 1 | 4 | 1.00 | 1.00 | 0.25 |
| 2018 | SIDS | 1 | 1.00 | 1.00 | 0.00 |
| 2019 | SIDS | 1 | 1.00 | 1.00 | 1.00 |
readr::write_csv(t, "../output/2x_hdi_number_references.csv")p <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, hdi_level) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
tidyr::pivot_longer(cols = -c("Year", "hdi_level"),
names_to = "Type", values_to = "Prop") %>%
dplyr::mutate(Key = factor(dplyr::case_when(Type == "climate_prop" ~ "Climate",
Type == "intersection_prop" ~ "Intersection",
Type == "health_prop" ~ "Health"),
levels = c("Intersection", "Climate", "Health")),
Year = lubridate::ymd(Year, truncated = 2L),
hdi_level = factor(hdi_level, levels = c("Very high human development",
"High human development",
"Medium human development",
"Low human development"))) %>%
dplyr::filter(Key == "Intersection" & !is.na(hdi_level)) %>%
ggplot(aes(x = Year, y = Prop, color = hdi_level)) +
geom_line() +
theme_minimal() +
labs(y = "Proportion of companies, %",
color = "") +
scale_y_continuous(labels = scales::percent_format(), limits = c(0, 1))
pggsave("../output/2y_hdi_prop_references.pdf", p, width = 10, height = 7)t <- total_counts %>%
dplyr::filter(Year >= 2011 & Sector == "Health Care Equipment & Ser...") %>%
dplyr::mutate(climate_n = ifelse(climate_count >= 1, 1, 0),
health_n = ifelse(health_count >= 1, 1, 0),
intersection_n = ifelse(intersection_count >= 1, 1, 0)
) %>%
dplyr::group_by(Year, hdi_level) %>%
dplyr::summarize(total_counts = n(),
climate_prop = round(sum(climate_n, na.rm = T)/total_counts,2),
health_prop = round(sum(health_n, na.rm = T)/total_counts,2),
intersection_prop = round(sum(intersection_n, na.rm = T)/total_counts,2)
) %>%
dplyr::arrange(desc(hdi_level), Year) %>%
dplyr::filter(!is.na(hdi_level))
colnames(t) <- c("Year", "HDI Level", "Total documents", "Climate", "Health", "Intersection")
t %>%
knitr::kable() %>%
kableExtra::kable_styling()| Year | HDI Level | Total documents | Climate | Health | Intersection |
|---|---|---|---|---|---|
| 2011 | Very high human development | 9 | 0.67 | 1.00 | 0.00 |
| 2012 | Very high human development | 13 | 0.54 | 0.92 | 0.15 |
| 2013 | Very high human development | 22 | 0.36 | 0.86 | 0.18 |
| 2014 | Very high human development | 21 | 0.43 | 0.81 | 0.14 |
| 2015 | Very high human development | 17 | 0.65 | 0.94 | 0.24 |
| 2016 | Very high human development | 22 | 0.55 | 0.82 | 0.18 |
| 2017 | Very high human development | 26 | 0.65 | 0.88 | 0.12 |
| 2018 | Very high human development | 26 | 0.85 | 0.96 | 0.42 |
| 2019 | Very high human development | 29 | 0.83 | 0.97 | 0.41 |
| 2020 | Very high human development | 37 | 0.70 | 0.89 | 0.41 |
| 2011 | Medium human development | 4 | 0.25 | 1.00 | 0.00 |
| 2012 | Medium human development | 2 | 0.50 | 1.00 | 0.50 |
| 2013 | Medium human development | 3 | 0.00 | 1.00 | 0.00 |
| 2014 | Medium human development | 5 | 0.20 | 1.00 | 0.20 |
| 2015 | Medium human development | 6 | 0.17 | 1.00 | 0.17 |
| 2016 | Medium human development | 4 | 0.25 | 1.00 | 0.25 |
| 2017 | Medium human development | 3 | 0.00 | 1.00 | 0.00 |
| 2018 | Medium human development | 3 | 0.00 | 1.00 | 0.00 |
| 2019 | Medium human development | 4 | 0.00 | 1.00 | 0.00 |
| 2020 | Medium human development | 2 | 0.50 | 1.00 | 0.00 |
| 2016 | Low human development | 1 | 0.00 | 1.00 | 0.00 |
| 2017 | Low human development | 1 | 0.00 | 1.00 | 0.00 |
| 2018 | Low human development | 1 | 0.00 | 1.00 | 0.00 |
| 2019 | Low human development | 2 | 0.00 | 1.00 | 0.00 |
| 2020 | Low human development | 1 | 0.00 | 1.00 | 0.00 |
| 2011 | High human development | 1 | 1.00 | 1.00 | 0.00 |
| 2012 | High human development | 3 | 0.33 | 0.67 | 0.33 |
| 2013 | High human development | 7 | 0.43 | 0.43 | 0.14 |
| 2014 | High human development | 4 | 0.00 | 0.50 | 0.00 |
| 2015 | High human development | 7 | 0.29 | 0.57 | 0.00 |
| 2016 | High human development | 5 | 0.60 | 0.40 | 0.20 |
| 2017 | High human development | 4 | 0.50 | 0.50 | 0.00 |
| 2018 | High human development | 7 | 0.71 | 0.57 | 0.29 |
| 2019 | High human development | 7 | 0.71 | 0.86 | 0.14 |
| 2020 | High human development | 6 | 0.83 | 0.83 | 0.33 |
readr::write_csv(t, "../output/2y_hdi_prop_references.csv")